Geometric reasoning for the extraction of form features

Qamhiyah, Abir Z.; Venter, R. D.; Benhabib, B.

doi:10.1016/0010-4485(96)00015-2

Cited by 26 publications

(9 citation statements)

References 11 publications

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“…(a2) show an example object with non-trivial feature interactions and its depression. In (b) the solid lines show the cavity graph of the depression, which [3,11] means that two faces 3 and 11 are uniÿed and are represented by one node. Only if the original cavity graph is augmented with three virtual links as shown in (b) by dashed links the form features of the object can be correctly identiÿed and extracted.…”

Section: Resultsmentioning

confidence: 99%

“…Most machine operations require high level semantic features such as pockets, slots, and holes instead of these low level geometric entities. Therefore, in order to achieve true manufacturing automation, several di erent mechanisms have been proposed to automatically extract the high level semantic features from the low level entities in solid model representations [1][2][3][4][5][6][7][8][9][10][11]. While these mechanisms can successfully recognize decoupled or isolated primitive features, they have achieved very limited success in identifying and describing compound depressions generated by the interaction of several semantic features [5,[12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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A Dempster–Shafer approach for recognizing machine features from CAD models

Marefat

2003

Pattern Recognition

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Dempster–Shafer approach for recognizing machine features from CAD models

Marefat

2003

Pattern Recognition

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“…Case et al [7] implemented such a classification for CAD/CAM integration and using a similar feature classification scheme, Xu and Hinduja [57] proposed a feature recognition approach to recognize rough machining features in 2-1/2D components. A boundarybased technique for feature extraction is presented by Qamhiyah et al [36]. A multiprocessor algorithm for fast feature recognition is presented in Regli et al [38].…”

Section: Form-feature Recognitionmentioning

confidence: 98%

Feature-Based Design – An Overview

Shahin¹

2008

Computer-Aided Design and Applications

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This paper presents an overview of the research carried out in feature-based design (FBD). Detailed definitions of features are presented and a summary on the work that has been carried out in areas such as feature taxonomies, mapping, validation, constraints in FBD, interfaces, engineering information storing schemes and the object-oriented approach is provided. A way forward is explored and a recommendation for further research is suggested in order to minimize production lead time and cut down capital costs by introducing and implementing methodologies for the automation of complex model creation.

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“…Venuvinod and Wong [25] used the advantages of faceedge adjacency graphs and expert systems to extract features by partitioning multi-attributed adjacency graphs into sub-graphs. Qamhiyah et al [26] demonstrated a technique to extract form features sequentially from B-rep models by interrogating its loops and planar faces, but this is not suitable for non-planar faces of the surfaces of the objects. Gadh and Prinz [27] developed a new "filter-function approach" to recognise different types of geometric features.…”

Section: Related Workmentioning

confidence: 99%

Automatic recognition of machining features from a solid model using the 2D feature pattern

Ranjan¹,

Pandey²,

Tiwari³

2005

Int J Adv Manuf Technol

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Feature recognition systems are now widely identified as a cornerstone for conceiving an automated process planning system. Various techniques have been reported in the literature, but a few of them acquired a status of generic methodology. A flexible and robust approach is demanded for recognising a wide variety of features, e.g., non-interacting, interacting circular and slanting features. This research aims to exploit the concept of the ray -firing technique, in which a 2D surface pattern for each feature is generated and information is extracted from these patterns to correlate it with the corresponding machining features. The system first defines a virtual surface and then probing rays are dropped from each point of this surface to the 2.5D features of the B-rep solid model. According to the length of rays between the bottom face of the 2.5D machining features and the virtual surface, 2D feature patterns are formed for each machining feature. Finally, features are recognised using an algorithm described in this article. Different types of examples have been considered to demonstrate the effectiveness of the proposed approach.

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Geometric reasoning for the extraction of form features

Cited by 26 publications

References 11 publications

A Dempster–Shafer approach for recognizing machine features from CAD models

A Dempster–Shafer approach for recognizing machine features from CAD models

Feature-Based Design – An Overview

Automatic recognition of machining features from a solid model using the 2D feature pattern

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